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Open Access

Evolutionary Experience-Driven Particle Swarm Optimization with Dynamic Searching

School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Khomeini Shahr Branch, Islamic Azad University, Isfahan 86145-311, Iran
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Abstract

Particle swarm optimization (PSO) algorithms have been successfully used for various complex optimization problems. However, balancing the diversity and convergence is still a problem that requires continuous research. Therefore, an evolutionary experience-driven particle swarm optimization with dynamic searching (EEDSPSO) is proposed in this paper. For purpose of extracting the effective information during population evolution, an adaptive framework of evolutionary experience is presented. And based on this framework, an experience-based neighborhood topology adjustment (ENT) is used to control the size of the neighborhood range, thereby effectively keeping the diversity of population. Meanwhile, experience-based elite archive mechanism (EEA) adjusts the weights of elite particles in the late evolutionary stage, thus enhancing the convergence of the algorithm. In addition, a Gaussian crisscross learning strategy (GCL) adopts cross-learning method to further balance the diversity and convergence. Finally, extensive experiments use the CEC2013 and CEC2017. The experiment results show that EEDSPSO outperforms current excellent PSO variants.

Keywords

particle swarm optimizationexperience-based topology structureelite archiveGaussian crisscross learning

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Complex System Modeling and Simulation
Volume 3 Issue 4,
December 2023
Pages 307-326
DOI: 10.23919/CSMS.2023.0015
Cite this article:
Li W, Jing J, Chen Y, et al. Evolutionary Experience-Driven Particle Swarm Optimization with Dynamic Searching. Complex System Modeling and Simulation, 2023, 3(4): 307-326. https://doi.org/10.23919/CSMS.2023.0015

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Received: 11 April 2023
Revised: 25 June 2023
Accepted: 05 July 2023
Published: 07 December 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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